GEO DATA

Data Articles

Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data: DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim; GEO DATA. 2025;7(1):18-26. Published online February 5, 2025; DOI: https://doi.org/10.22761/GD.2024.0052

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Abstract PDF: In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R² core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.

Deep Learning Training Data for Phase Unwrapping of SAR Interferograms: Won-Kyung Baek, Hyung-Sup Jung; GEO DATA. 2024;6(4):463-470. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0051

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Abstract PDF: Phase unwrapping is an essential process in synthetic aperture radar interferometry that restores phase signals constrained within the range of (-π, π) to their true phase values. Traditional algorithm-based methods can introduce significant errors due to rapid and steep phase gradient or noise, which negatively impact terrain elevation and surface displacement analyses. To overcome these limitations, deep learningbased phase unwrapping techniques have been proposed; however, there has been insufficient previous studies due to the lack of accurate training and test data. This paper aims to share the training data generated using the phase unwrapping simulation method with locally-different phase noise. The data were generated by the simulation of topographic phases and phase noise, atmospheric and orbital distortions. Additionally, data augmentation for phase variation and noise levels was applied to address data imbalance issues. The shared data consists of two types: one with a constant phase noise level for each patch, and another that simulates locally different phase noise based on augmented coherence data. This data is primarily effective for unwrapping topographic phase components and holds significance as the first phase unwrapping training data of synthetic aperture radar interferograms shared in Korea. We expect this resource to serve as foundational data for future phase unwrapping technology research, including applications for upcoming satellites like KOMPSAT-6 and water resource satellites.

Data on Time-series Observation of Ground Subsidence in South Korea Using Sentinel-1 SAR Observations: Chanuk Lee, Jeongheon Ju, JangHun Kang, Seowon Kim, HeeJin An, Yuna Hong, Seokyeong Hwang, Sang-Hoon Hong; GEO DATA. 2024;6(4):495-504. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0048

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Abstract PDF: Ground subsidence is a phenomenon where surface materials sink due to a combination of natural and anthropogenic factors. South Korea has experienced human casualties and economic losses due to ground subsidence, such as sinkholes. Moreover, with the recent increase in earthquakes in the country, the importance of collecting and analyzing data for monitoring ground subsidence and surface displacement for disaster prevention is growing. This study monitored ground subsidence that occurred in South Korea from January 1, 2021 to December 31, 2022, while also observing other surface displacements. The study utilized synthetic aperture radar (SAR) satellite data, which, due to its high penetration capabilities of microwaves, is relatively unaffected by weather and day-night conditions, enabling wide-area observation with high spatial resolution, making it suitable for monitoring surface displacements. A total of 321 C-band Sentinel-1 SAR images, obtained between January 1, 2021 and December 31, 2022, were analyzed. Based on a perpendicular baseline distance of 200 meters and a time interval of 100 days, small baseline subset network were created. Time-series surface displacement data and velocity maps were produced to analyze the overall displacement patterns in the study areas.

Evaluation of Residual Phase from Orbit Accuracy Using TerraSAR-X/TanDEM-X SAR Observation: Yeojin Kim, Sang-Hoon Hong; GEO DATA. 2024;6(4):487-494. Published online December 31, 2024; DOI: https://doi.org/10.22761/GD.2024.0039

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Abstract PDF: Interferometric synthetic aperture radar (InSAR) is used to observe precise surface displacement and create digital elevation models by calculating the phase differences between two or more SAR images obtained over the same surface area. The phase of a repeat-pass interferogram can be expressed as the sum of contributions from topography, ground displacement, earth curvature, noise, and the satellite’s orbital phase component. For precise observations, removing unnecessary phase components is essential. Errors owing to the satellite’s orbit accuracy leave residual phases in the interferogram, which become a significant limitation for wide-area ground displacement monitoring using the InSAR technique. This study used four pairs of images acquired by TerraSAR-X in monostatic pursuit mode from October 2014 to February 2015 to analyze the residual phase caused by orbital errors. Since these images were acquired with a 10-second interval between the TerraSAR-X and TanDEM-X satellites, the phase coherence was maintained over time. The Tarim Basin in China was selected as the study area to minimize the impact of terrain distortion. By introducing a 0.5 m error into the x, y, and z components of the satellite position vectors and creating differential interferograms, it was found that the x component’s orbital error caused the largest residual phase, with linear residual phases observed in the north-south direction. Furthermore, various baselines ranging from -29.71 to 263.21 m were used to quantitatively compare the residual phases caused by orbital errors based on the perpendicular baseline. The residual phase was similar across the four differential interferograms, with approximately 3.49 π for the x component, 0.85 π for the y component, and 1.25 π for the z component. The residual phase resulting from simulated orbital errors was effectively mitigated using a 2D quadratic model.

Original Papers

The Cheonji Lake GeoAI Dataset Based in Synthetic Aperture Radar Images: TerraSAR-X, Sentinel-1 and ALOS PALSAR-2: Eu-Ru Lee, Ha-Seong Lee, Ji-Min Lee, Sun-Cheon Park, Hyung-Sup Jung; GEO DATA. 2023;5(4):251-261. Published online December 29, 2023; DOI: https://doi.org/10.22761/GD.2023.0056

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2 Citations

Abstract PDF

The fluctuations in the area and level of Cheonji in Baekdu Mountain have been employed as significant indicators of volcanic activity. Monitoring these changes directly in the field is challenging because of the geographical and spatial features of Baekdu Mountain. Therefore, remote sensing technology is crucial. Synthetic aperture radar utilizes high-transmittance microwaves to directly emit and detect the backscattering from objects. This weatherproof approach allows monitoring in every climate. Additionally, it can accurately differentiate between water bodies and land based on their distinct roughness and permittivity characteristics. Therefore, satellite radar is highly suitable for monitoring the water area of Cheonji. The existing algorithms for classifying water bodies using satellite radar images are significantly impacted by speckle noise and shadows, resulting in frequent misclassification. Deep learning techniques are being utilized in algorithms to accurately compute the area and boundary of interest in an image, surpassing the capabilities of previous algorithms. This study involved the creation of an AI dataset specifically designed for detecting water bodies in Cheonji. The dataset was constructed using satellite radar images from TerraSAR-X, Sentinel-1, and ALOS-2 PALSAR-2. The primary objective was to accurately detect the area and level of water bodies. Applying the dataset of this study to deep learning techniques for ongoing monitoring of the water bodies and water levels of Cheonji is anticipated to significantly contribute to a systematic method for monitoring and forecasting volcanic activity in Baekdu Mountain.

Citations

Citations to this article as recorded by

Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model
Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Korean Journal of Remote Sensing.2024; 40(5-1): 507. CrossRef
GeoAI Dataset for Urban Water Body Detection Using TerraSAR-X Satellite Radar Imagery
Eu-Ru Lee, Jun-Hyeok Jung, Ki-Chang Kim, Seong-Jae Yu, Hyung-Sup Jung
GEO DATA.2024; 6(4): 435. CrossRef

Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data: Jaese Lee, Dukwon Bae, Young Jun Kim, Jungho Im; GEO DATA. 2023;5(3):119-125. Published online September 25, 2023; DOI: https://doi.org/10.22761/GD.2023.0025

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Abstract PDF: Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.

A Study on C-band Synthetic Aperture Radar Soil Moisture Estimation Based on Machine Learning Using Soil Physics, Topography, and Hydrological Information: Jeehun Chung, Yonggwan Lee, Jinuk Kim, Wonjin Jang, Seongjoon Kim; GEO DATA. 2023;5(3):137-146. Published online September 22, 2023; DOI: https://doi.org/10.22761/GD.2023.0026

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Abstract PDF: In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.

Article

Ground-based data from wheat cropping fields in Australia for development of soil moisture retrieval algorithm using satellite images: SeungJae Lee, SunGu Lee, Dongryeol Ryu; GEO DATA. 2020;2(2):1-4. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.001

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Abstract PDF: Soil moisture is an important data which can be used for crop growth estimation, drought prediction, irrigation, and development of hydrological model. However, it is difficult to obtain soil moisture data from inaccessible area or very large area using only general field campaign. For this reason, many soil moisture retrieval algorithms have been developed based on satellite remote sensing technique. It should be noted that both satellite images and ground-based data for the region of interest are required to effectively develop the soil moisture retrieval algorithm using satellite images. Thus, Korea aerospace research institute, KARI, have collected ground-based data containing soil moisture, soil temperature, and crop height in collaboration with the university of Melbourne from wheat cropping fields in Australia which are suitable for the development of soil moisture retrieval algorithm. The ground-based data was collected from wheat cropping fields containing various types of soils for about 7 months from May 2019 to November 2019.

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